SpringerLink
Log in

Real-time simulation for long paths in laser-based additive manufacturing: a machine learning approach

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This study predicts in real time the evolution of temperature and density for arbitrary long tracks in laser-based additive manufacturing using artificial neural networks (ANNs). First, a random laser path is transformed appropriately to serve as an ANN input via a custom trajectory decomposition method providing a local description of each trajectory point relative to its surroundings, which is calculated and load-optimized using K-d trees. For each trajectory point, a “Master” ANN calculates the present temperature, using the trajectory descriptors and previous step temperature as inputs. This is made into a parallel procedure by exploiting a “Pilot” ANN without temperature feedback, making a first pass over the whole trajectory to estimate temperature responses. The Master ANN uses these results as a feedback that is iteratively refined. A “Rider” ANN uses the final temperatures as input and calculates local density evolution. Four hundred fifty finite element model experiments of short random walk trajectories were used for ANN training. Results are presented for various types of laser paths, including random, hatch, spiral, fractal, and spline. ANN simulation execution time consistently taking under 50% of the real process time in all validated cases for track length in excess of 100 m proved the ability of the platform to operate at the full layer-scale of LBAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Aboutaleb AM, Bian L, Elwany A, Shamsaei N, Thompson SM, Tapia G (2017) Accelerated process optimization for laser-based additive manufacturing by leveraging similar prior studies. IISE Trans 49:31–44. https://doi.org/10.1080/0740817X.2016.1189629

    Article  Google Scholar 

  2. Francois MM, Sun A, King WE, Henson NJ, Tourret D, Bronkhorst CA, Carlson NN, Newman CK, Haut T, Bakosi J, Gibbs JW, Livescu V, Vander Wiel SA, Clarke AJ, Schraad MW, Blacker T, Lim H, Rodgers T, Owen S, Abdeljawad F, Madison J, Anderson AT, Fattebert J-L, Ferencz RM, Hodge NE, Khairallah SA, Walton O (2017) Modeling of additive manufacturing processes for metals: challenges and opportunities. Curr Opin Solid State Mater Sci 21:198–206. https://doi.org/10.1016/j.cossms.2016.12.001

    Article  Google Scholar 

  3. Ponche R, Kerbrat O, Mognol P, Hascoet J (2014) A novel methodology of design for additive manufacturing applied to additive laser manufacturing process. Robot Comput Integr Manuf 30:389–398. https://doi.org/10.1016/j.rcim.2013.12.001

    Article  Google Scholar 

  4. King W, Anderson AT, Ferencz RM, Hodge NE, Kamath C, S a K (2015) Overview of modelling and simulation of metal powder bed fusion process at Lawrence Livermore National Laboratory. Mater Sci Technol 31:957–968. https://doi.org/10.1179/1743284714Y.0000000728

    Article  Google Scholar 

  5. Mani M, Lane BM, Donmez MA, Feng SC, Moylan SP (2017) A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes. Int J Prod Res 55:1400–1418. https://doi.org/10.1080/00207543.2016.1223378

    Article  Google Scholar 

  6. Martukanitz R, Michaleris P, Palmer T, DebRoy T, Liu Z-K, Otis R, Heo TW, Chen L-Q (2014) Toward an integrated computational system for describing the additive manufacturing process for metallic materials. Addit Manuf 1(4):52–63. https://doi.org/10.1016/j.addma.2014.09.002

    Article  Google Scholar 

  7. Jared BH, Aguilo MA, Beghini LL, Boyce BL, Clark BW, Cook A, Kaehr BJ, Robbins J (2017) Additive manufacturing: toward holistic design. Scr Mater 135:141–147. https://doi.org/10.1016/j.scriptamat.2017.02.029

    Article  Google Scholar 

  8. Stucker B (2013) Recent trends in additive manufacturing & the need for predictive simulation. Manuf Lett 1:38–41

    Article  Google Scholar 

  9. Williams JD, Deckard CR (1998) Advances in modeling the effects of selected parameters on the SLS process. Rapid Prototyp J 4:90–100

    Article  Google Scholar 

  10. Lavery NP, Brown SGR, Sienz J, Cherry J (2014) A review of computational modelling of additive layer manufacturing – multi-scale and multi-physics. Sustain Des Manuf 1:651–673. https://doi.org/10.13140/RG.2.1.3103.0884

    Article  Google Scholar 

  11. Megahed M, Mindt H-W, N’Dri N, Duan H, Desmaison O (2016) Metal additive-manufacturing process and residual stress modeling. Integrating Materials and Manufacturing Innovation

  12. Zeng K, Pal D, Patil N, Stucker BE (2013) A new dynamic mesh method applied to the simulation of selective laser melting. Proc Solid Free Fabr Symp:549–559

  13. Pal D, Patil N, Zeng K, Stucker B (2014) An integrated approach to additive manufacturing simulations using physics based, coupled multiscale process modeling. J Manuf Sci Eng 136:061022. https://doi.org/10.1115/1.4028580

    Article  Google Scholar 

  14. Keller N, Ploshikhin V (2014) New method for fast predictions of residual stress and distortion of AM parts. Solid Free Fabr Symp Austin, Texas 25

  15. Stathatos E, Vosniakos GC (2017) A computationally efficient universal platform for thermal numerical modeling of laser-based additive manufacturing. Proc Inst Mech Eng C J Mech Eng Sci 232:2317–2333. https://doi.org/10.1177/0954406217720230

    Article  Google Scholar 

  16. Boillat E, Kolossov S, Glardon R, Loher M, Saladin D, Levy G (2004) Finite element and neural network models for process optimization in selective laser sintering. Proc Inst Mech Eng B J Eng Manuf 218:607–614. https://doi.org/10.1243/0954405041167121

    Article  Google Scholar 

  17. Shen X, Yao J, Wang Y, Yang J (2004) Density prediction of selective laser sintering parts based on artificial neural network. In: Yin F-L, Wang J, Guo C (eds) Advances in neural networks - ISNN 2004. Springer Berlin Heidelberg, Berlin, pp 832–840

    Chapter  Google Scholar 

  18. Fathi A, Mozaffari A (2014) Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map. J Intell Manuf 25:775–795. https://doi.org/10.1007/s10845-012-0718-6

    Article  Google Scholar 

  19. Negi S, Sharma RK (2016) Study on shrinkage behaviour of laser sintered PA 3200GF specimens using RSM and ANN. Rapid Prototyp J 22:645–659. https://doi.org/10.1108/RPJ-08-2014-0090

    Article  Google Scholar 

  20. Mohamed O, Masood S, Bhowmik J (2016) Analytical modelling and optimization of the temperature-dependent dynamic mechanical properties of fused deposition fabricated parts made of PC-ABS. Materials (Basel) 9. https://doi.org/10.3390/ma9110895

    Article  Google Scholar 

  21. Vahabli E, Rahmati S (2016) Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Precis Eng Manuf 17:1589–1603. https://doi.org/10.1007/s12541-016-0185-7

    Article  Google Scholar 

  22. Kamath C (2016) Data mining and statistical inference in selective laser melting. Int J Adv Manuf Technol 86:1659–1677. https://doi.org/10.1007/s00170-015-8289-2

    Article  Google Scholar 

  23. Garg A, Lam JSL, Savalani MM (2018) Laser power based surface characteristics models for 3-D printing process. J Intell Manuf 29:1191–1202. https://doi.org/10.1007/s10845-015-1167-9

    Article  Google Scholar 

  24. Beale MH, Hagan MT, Demuth HB (2016) Neural network toolbox (TM) user’s guide. MathWorks 1–558. https://doi.org/10.1002/0471221546

    Google Scholar 

  25. Yang J, Bin H, Zhang X, Liu Z (2003) Fractal scanning path generation and control system for selective laser sintering (SLS). Int J Mach Tools Manuf 43:293–300. https://doi.org/10.1016/S0890-6955(02)00212-2

    Article  Google Scholar 

  26. Ma L, Bin H (2007) Temperature and stress analysis and simulation in fractal scanning-based laser sintering. Int J Adv Manuf Technol 34:898–903. https://doi.org/10.1007/s00170-006-0665-5

    Article  Google Scholar 

  27. Yu J, Lin X, Ma L, Wang J, Fu X, Chen J, Huang W (2011) Influence of laser deposition patterns on part distortion, interior quality and mechanical properties by laser solid forming (LSF). Mater Sci Eng A 528:1094–1104. https://doi.org/10.1016/j.msea.2010.09.078

    Article  Google Scholar 

  28. ** GQ, Li WD, Tsai CF, Wang L (2011) Adaptive tool-path generation of rapid prototy** for complex product models. J Manuf Syst 30:154–164. https://doi.org/10.1016/j.jmsy.2011.05.007

    Article  Google Scholar 

  29. ** GQ, Li WD, Gao L, Popplewell K (2013) A hybrid and adaptive tool-path generation approach of rapid prototy** and manufacturing for biomedical models. Comput Ind 64:336–349. https://doi.org/10.1016/j.compind.2012.12.003

    Article  Google Scholar 

  30. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133. https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  31. Teti R, Kumara SRT (1997) Intelligent computing methods for manufacturing systems. CIRP Ann Manuf Technol 46:629–652. https://doi.org/10.1016/S0007-8506(07)60883-X

    Article  Google Scholar 

  32. Belongie S, Malik J, Puzicha J (2000) Shape context: a new descriptor for shape matching and object recognition. Adv Neural Inf Proces Syst 546:831–837 10.1.1.27.8567

    Google Scholar 

  33. Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7:1–25. https://doi.org/10.1137/S1052623493250780

    Article  MathSciNet  MATH  Google Scholar 

  34. Omotehinwa T, Ramon S (2013) Fibonacci numbers and golden ratio in mathematics and science. Int J Comput Inf Technol ISSN 02:630–638

    Google Scholar 

  35. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18:509–517. https://doi.org/10.1145/361002.361007

    Article  MATH  Google Scholar 

  36. Huang H, Cui C, Cheng L, Liu Q, Wang J (2012) Grid interpolation algorithm based on nearest neighbor fast search. Earth Sci Inf 5:181–187. https://doi.org/10.1007/s12145-012-0106-y

    Article  Google Scholar 

Download references

Funding

This work was financially supported by a 4-year NTUA scholarship of E. Stathatos.

Author information

Authors and Affiliations

  1. Section of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytechniou 9, Zografou, 15780, Athens, Greece

    Emmanuel Stathatos & George-Christopher Vosniakos

Authors
  1. Emmanuel Stathatos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George-Christopher Vosniakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George-Christopher Vosniakos.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stathatos, E., Vosniakos, GC. Real-time simulation for long paths in laser-based additive manufacturing: a machine learning approach. Int J Adv Manuf Technol 104, 1967–1984 (2019). https://doi.org/10.1007/s00170-019-04004-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04004-6

Keywords

Search

Navigation